Alexithymia and illness perceptions in persons with multiple sclerosis and their partners
Bibliographic record
Abstract
Illness perceptions (IPs) encompass opinions regarding the nature, severity and curability of a disease. The aim of this cross-sectional study was to investigate the association between alexithymia and IPs among persons with multiple sclerosis (PwMS) and their partners, as well as within the dyads composed of PwMS and partners. PwMS referred to the Multiple Sclerosis Center of the University Hospital "Policlinico-San Marco" from 11th August 2021 to 7th January 2022 and their partners completed a battery of questionnaires, including the Toronto Alexithymia Scale-20 and the Illness Perception Questionnaire Revised. A dyadic data analysis (Actor-Partner Interdependence Model) was performed to test the effect of alexithymic traits both on a person's own illness perceptions (actor effect) and on the partner's illness perceptions (partner effect). 100 PwMS (71 women; mean age 47.6 ± 10.4 years) and 100 partners (29 women; mean age 49.1 ± 10.8 years), with a mean partnership duration of 20.1 ± 11.7 years, were enrolled. At the dyadic analysis, statistically significant small-to-moderate actor and partner effects were found considering alexithymia (total score and alexithymic facets) and IPs, whereby higher alexithymic traits related to higher negative perceptions (i.e. consequences, emotional representations) and lower positive ones (i.e. coherence, treatment control). Our findings support the relationship between alexithymia and negative illness appraisals. This data may inform therapeutic interventions aimed at reducing alexithymic traits, which in turn may reduce negative, and potentially dysfunctional, illness perceptions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".